11 May 2021

Introduction

  • The current COVID-19 pandemia, caused by the SARS-CoV-2 virus, has caused the death of more than 3.2 million people worldwide.

  • Studies in convalescent patients has shown a decline of antibody-mediated response a few months after infection.

  • CD8+ T cells are critical for efficient clearance of virus-infected cells and long-term protection against SARS-CoV-2.

  • DTU HealthTech Department analyzed T cell recognition towards SARS-CoV-2-derived peptides in COVID-19 patients and healthy individuals using DNA-barcoded pMHC multimers combined with a T cell phenotype panel.

Objective

  • The project utilizes flow cytometry data published by Saini et al., 2021, to make a T cell phenotype analysis by comparing the expression of cell surface markers in COVID-19 patients and healthy individuals to better understand T cell immunity to SARS-CoV-2 infection.

  • The COVID-19 pandemic is caused by the highly infectious virus; SARS-CoV-2.

  • CD8+ T cells perform essential functions in the control and elimination of viral infections

  • Here, we analyze the immunophenotypes of CD8 T cells that can recognize peptides from the SARS-CoV-2 genome

  • We compare COVID-19 patients with healthy donors (HD) never exposed to the virus.

  • Furthermore, we compare the phenotypes to the severity of the disease.

  • We also compare the phenotype of the SARS-CoV-2 specific CD8 T cells with T cells that recognize common viruses.

Materials and methods

Phenotype data

  • Phenotyping of immune cells provides imformation abouts the activation status of the cells
  • This is obtained through antibody staining and flow cytometry, which transforms a fluorescent signal into a voltage.
  • Cell populations of specific phenotype is gated in FlowJo software, and frequencies of positive cells can afterwards exported as an .xlsx file

Raw data

  • The dataset is obtained from S. K. Saini et al., Sci. Immunol. (2021)
## # A tibble: 82 x 25
##    SampleID  Hospital_status `lymphocytes/sin… `lymphocytes/si… `lymphocytes/si…
##    <chr>     <chr>           <chr>             <chr>            <chr>           
##  1 Patient_1 Hospitalized    28.7              20.399999999999… 1.66            
##  2 Patient_2 Hospitalized    12.5              84.1             44.9            
##  3 Patient_3 Hospitalized    27.3              91.2             16.7            
##  4 Patient_4 Hospitalized    5.9               23.3             8.4600000000000…
##  5 Patient_5 Hospitalized    16.8              40               11.2            
##  6 Patient_6 Hospitalized    4.83              49.2             25.9            
##  7 Patient_7 NA              NA                76.7             32.1            
##  8 Patient_8 Hospitalized    1.94              45.6             5.41            
##  9 Patient_9 Hospitalized    3.76              34.1             6.15            
## 10 Patient_… Hospitalized    1.69              31.5             12              
## # … with 72 more rows, and 20 more variables:
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD69 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/PD-1 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/HLA-DR <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD27 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD57 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA-_CCR7+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA-_CCR7- <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA+_CCR7- <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA+_CCR7+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_CD39+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_CD69+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_HLA-DR+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_PD-1+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD38 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD39 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD69 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/PD-1 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/HLA-DR <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD27 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD57 <chr>

Data wrangling

Tidying the data set

  • Change NA values
  • Add cohort group as a new variable
  • Collapse multiple columns into two columns: Gating and Fraction
  • Splitting the gating path into two new columns: Parent and Last population
## # A tibble: 1,886 x 8
##    SampleID  cohort_type Hospital_status Gating          T_cell Parent_populati…
##    <chr>     <chr>       <chr>           <chr>           <chr>  <chr>           
##  1 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    CD8+            
##  2 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  3 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  4 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  5 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  6 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  7 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  8 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  9 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
## 10 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
## # … with 1,876 more rows, and 2 more variables: Last_population <chr>,
## #   Fraction <dbl>

FCS files

  • .fcs files are the raw data files obtained with flow cytometry.
  • In addition to manual gating, these files can be used for clustering and visualizing the cells’ expression levels of multiple parapeters in 2 dimensions with UMAP (Uniform Manifold Approximation and Projection).

Visualizing the data

  • Reading covid_data_augment
  • Filtering on Parent and Last population
  • Generate plot using ggplot - Boxplot and dotplot
  • Multiple plots viewed as facetwrap
  • Used geomsigif with Kruskal test

Results

Figure A

Figure B + C

Figure D

Figure E

Figure F

  • Used geomsignif with Kruskal test

Figures - S7B and S8C

Figure S7 B

Figure S8 C

UMAP visualization

Conclusion

======= ## DISCUSSION